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<p>Biophysical signals from the measurement of bacterial cellular vibrations and metabolic activity is measurable by microscopy. The dynamics are critical in the development of antimicrobial studies, especially in tests of drug effectiveness and resistance. The proposed study was expected to measure the trends in bacterial cellular activity suppression by AI-powered motion tracking and compared the effects of polymyxin-B and ampicillin on bacterial vibrational responses. The study aimed to improve the modeling of the response to antibiotics by comparing actual data of the microscopic activity with the predictions made by AI, providing information about the dynamic of bacterial inhibition. This work combined the technique of Optical Flow analysis and suppression modeling, which allowed to quantify the motion automatically. The trends of bacterial cellular activities were examined prior to, during, and following exposure to antibiotics in order to establish the patterns of suppression and compare AI predictions with experimental findings through the use of normalized Root Mean Square error or NRMSE analysis with 95% confidence interval. The conclusions of the study shown that AI based models naturally recapitulated the bacterial cellular activity inhibition trends albeit with high concordance between real and predicted suppression curves (PMB 3 and ampicillin 2: NRMSE: 15.2 and 16.8 respectively). But ampicillin 4 and 7 had greater values of NRMSE (18.9% and19.3%), which means they have more problems in capturing nonlinear inhibition behavior at late-stage cells lysis. AI-based cellular activity monitoring can be more accurate, efficient, and provide real-time classification of antimicrobial susceptibility. The procedure was about 22&#xa0;h quicker than the traditional antimicrobial susceptibility testing. To enhance the studies on the topic of antibiotic efficacy, future research must enhance suppression modeling by introducing AI learning methods that can be adaptive and providing more validity of the methodology to other bacterial strains.</p>

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Real-Time bacterial motion analysis under antibiotic stress: integrating AI for precision diagnostics

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Ali Adel Dawood

摘要

Biophysical signals from the measurement of bacterial cellular vibrations and metabolic activity is measurable by microscopy. The dynamics are critical in the development of antimicrobial studies, especially in tests of drug effectiveness and resistance. The proposed study was expected to measure the trends in bacterial cellular activity suppression by AI-powered motion tracking and compared the effects of polymyxin-B and ampicillin on bacterial vibrational responses. The study aimed to improve the modeling of the response to antibiotics by comparing actual data of the microscopic activity with the predictions made by AI, providing information about the dynamic of bacterial inhibition. This work combined the technique of Optical Flow analysis and suppression modeling, which allowed to quantify the motion automatically. The trends of bacterial cellular activities were examined prior to, during, and following exposure to antibiotics in order to establish the patterns of suppression and compare AI predictions with experimental findings through the use of normalized Root Mean Square error or NRMSE analysis with 95% confidence interval. The conclusions of the study shown that AI based models naturally recapitulated the bacterial cellular activity inhibition trends albeit with high concordance between real and predicted suppression curves (PMB 3 and ampicillin 2: NRMSE: 15.2 and 16.8 respectively). But ampicillin 4 and 7 had greater values of NRMSE (18.9% and19.3%), which means they have more problems in capturing nonlinear inhibition behavior at late-stage cells lysis. AI-based cellular activity monitoring can be more accurate, efficient, and provide real-time classification of antimicrobial susceptibility. The procedure was about 22 h quicker than the traditional antimicrobial susceptibility testing. To enhance the studies on the topic of antibiotic efficacy, future research must enhance suppression modeling by introducing AI learning methods that can be adaptive and providing more validity of the methodology to other bacterial strains.